An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

Background Preoperative assessment of pathologic complete response (pCR) in locally
advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT) is …

In the past few decades, according to the rapid development of information technology,
artificial intelligence (AI) has also made significant progress in the medical field. Colorectal …

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of
lack of a robust biomarker and heterogeneity between and within tumors. The aim of this …

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME)
represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal …

The high incidence of rectal cancer in both sexes makes it one of the most common tumors,
with significant morbidity and mortality rates. To define the best treatment option and …

Background Machine learning (ML) has been introduced in various fields of healthcare. In
colorectal surgery, the role of ML has yet to be reported. In this systematic review, an …

Purpose Our study investigated the contribution that the application of radiomics analysis on
post-treatment magnetic resonance imaging can add to the assessments performed by an …

Objective To interrogate the mesorectal fat using MRI radiomics feature analysis in order to
predict clinical outcomes in patients with locally advanced rectal cancer. Methods This …